Three-dimensional static underwater acoustic sensor system based on fuzzy logic

Abstract

The application discloses a three-dimensional static underwater acoustic sensor system based on fuzzy logic, comprising a first network node and a plurality of second network nodes, the first network node broadcasts a request forwarding message, the second network node receives the broadcast request forwarding message, carries out first fuzzy logic processing on a node parameter combination of itself, obtains a first fuzzy output variable of itself for broadcasting, the first network node receives each first fuzzy output variable, and determines one of the second network nodes as a next hop node according to each first fuzzy output variable. The application can make the first network node in each sending period calculate a moderate value through a fuzzy weight function to find a next hop node most suitable for forwarding the data packet, can effectively save energy consumption and data processing time in the case of high node density, and is favorable for improving overall link quality and realizing a routing protocol dominated by energy saving. The application is widely applied in the technical field of underwater acoustic sensors.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of underwater acoustic sensor, and particularly relates to a three-dimensional static underwater acoustic sensor system based on fuzzy logic. BACKGROUND

[0002] An underwater acoustic communication network can be used to collect data from positions such as the seabed and transmit the data to a target node through network nodes distributed in a water body. In order to achieve the communication goals of energy saving, high efficiency, accuracy and stability, a good routing protocol needs to be designed for the underwater acoustic communication network as rules and algorithms for underwater communication. The current routing protocol for the underwater acoustic communication network faces challenges such as large and time-varying transmission delay, channel multipath effect and large energy consumption. SUMMARY

[0003] In view of the technical problems of the current routing protocol for the underwater acoustic communication network, such as large and time-varying transmission delay, channel multipath effect and large energy consumption, the present application aims to provide a three-dimensional static underwater acoustic sensor system based on fuzzy logic.

[0004] The embodiment of the present application comprises a three-dimensional static underwater acoustic sensor system based on fuzzy logic, which comprises a plurality of network nodes, wherein the plurality of network nodes comprises a first network node and a plurality of second network nodes.

[0005] The first network node is configured to broadcast a request forwarding message.

[0006] The second network node is configured to receive the broadcast request forwarding message, obtain a node parameter combination of itself in response to the request forwarding message, perform first fuzzy logic processing on the node parameter combination, obtain a first fuzzy output variable of itself, and broadcast the first fuzzy output variable.

[0007] The first network node is configured to receive the first fuzzy output variable sent by each of the second network nodes, and determine one of the second network nodes as a next hop node according to each of the first fuzzy output variables.

[0008] Further, the obtaining of the node parameter combination of itself comprises:

[0009] obtaining a REN parameter, a DIS parameter, a DEF parameter, a PDR parameter, a PDRN parameter and a SNHN parameter of itself;

[0010] composing the node parameter combination of itself with the REN parameter, the DIS parameter, the DEF parameter, the PDR parameter, the PDRN parameter and the SNHN parameter of itself;

[0011] The REN parameter represents a residual energy value of the second network node, the DIS parameter represents an actual distance between the second network node and the first network node, the DEF parameter represents a shortest distance from the second network node to the first network node, the PDR parameter represents a packet delivery rate between the second network node and a primary candidate neighbor node of the second network node, the PDRN parameter represents a packet delivery rate between the primary candidate neighbor node of the second network node and a secondary candidate neighbor node, and the SNHN parameter represents a maximum value of a fuzzy output variable of the primary candidate neighbor node of the second network node.

[0012] Further, the first fuzzy logic processing on the node parameter combination is performed to obtain a first fuzzy output variable of the node parameter combination, and the first fuzzy output variable of the node parameter combination is obtained by performing the first fuzzy logic processing on the node parameter combination.

[0013] The membership functions corresponding to each parameter in the node parameter combination and the membership function corresponding to the first fuzzy output variable are obtained.

[0014] The first fuzzy output variable is obtained by performing calculation according to the membership functions.

[0015] Further, the second network node is determined as the next hop node according to the first fuzzy output variables, and the determination includes:

[0016] A maximum value in the first fuzzy output variables is determined.

[0017] The second network node sending the maximum value is determined as the next hop node.

[0018] Further, the second network node is determined as the next hop node according to the first fuzzy output variables, and the determination includes:

[0019] The second fuzzy output variable is obtained by performing second fuzzy logic processing on the first fuzzy output variables.

[0020] The second network node is determined as the next hop node according to the second fuzzy output variable.

[0021] Further, the second network node is determined as the next hop node according to the second fuzzy output variable, and the determination includes:

[0022] When the second fuzzy output variable is a first value, a maximum value in the first fuzzy output variables is determined, and the second network node sending the maximum value is determined as the next hop node.

[0023] When the second fuzzy output variable is a second value, each first fuzzy output variable is updated after waiting for a first time length, and a next hop node is determined according to the updated each first fuzzy output variable.

[0024] Further, the first network node is configured to obtain a to-be-forwarded data packet, and send the to-be-forwarded data packet to the next hop node.

[0025] Further, the plurality of network nodes comprise a sink node and a plurality of sensor nodes.

[0026] Further, any of the sensor nodes is configured to be the first network node, and the sink node or any of the sensor nodes is configured to be the second network node.

[0027] Further, the sink node is configured to be arranged on a water surface of a water body, each sensor node is configured to be arranged in an underwater three-dimensional space of the water body, and each sensor node is randomly distributed in the underwater three-dimensional space according to a Poisson distribution.

[0028] The three-dimensional static underwater acoustic sensor system based on fuzzy logic in the embodiment can enable the first network node in each sending cycle to calculate a moderate value by using a fuzzy weight function, so as to find a next hop node most suitable for forwarding the data packet, can effectively save energy consumption and data processing time in the case of high node density, and is beneficial to improving overall link quality and realizing a routing protocol dominated by energy saving. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 FIG. 1 is a structural schematic diagram of a three-dimensional static underwater acoustic sensor system based on fuzzy logic in the embodiment;

[0030] Figure 2 FIG. 2 is a schematic diagram of a first network node and a second network node in the embodiment;

[0031] Figure 3 FIG. 3 is a schematic diagram of steps executed by the first network node and the second network node in the embodiment;

[0032] Figure 4 FIG. 4 is a schematic diagram of a node parameter combination and a membership function of a first fuzzy output variable in the embodiment;

[0033] Figure 5 FIG. 5 is a schematic diagram of a membership function of a second fuzzy output variable in the embodiment;

[0034] Figure 6 FIG. 6 is a schematic diagram of sending a to-be-forwarded data packet to a next hop node in the embodiment. DETAILED DESCRIPTION

[0035] In this embodiment, the structure of the three-dimensional static underwater acoustic sensor system based on fuzzy logic is shown in Figure 1 Figure 1 The three-dimensional static underwater acoustic sensor system based on fuzzy logic is a single sink architecture, including multiple network nodes, which are a sink node and multiple sensor nodes respectively. When using the three-dimensional static underwater acoustic sensor system based on fuzzy logic, the sink node can be used to be set on the water surface of the water body, and each sensor node can be used to be set in the three-dimensional space underwater. Each sensor node can be randomly distributed according to the Poisson distribution in the three-dimensional space underwater, that is, if the positions of each sensor node underwater are expressed as coordinates in a unified coordinate system, the coordinates satisfy the Poisson distribution law.

[0036] In this embodiment, each sensor node has data acquisition capability and underwater acoustic communication capability, and the sink node has underwater acoustic communication capability. The sensor node can collect data by itself or receive data sent by other sensor nodes. The sensor node that collects or receives data as the current node forwards the data to the next hop node, that is, another sensor node or sink node, and finally sends the data to the sink node. Two sensor nodes or a sensor node and a sink node communicate through underwater acoustic.

[0037] For convenience of description, it is assumed that the three-dimensional static underwater acoustic sensor system based on fuzzy logic satisfies the following conditions: the sink node on the water surface has unlimited energy supply, and the energy of the sensor node underwater is limited; all sensor nodes can determine their own and the sink node's geographical position by using accurate positioning technology. Since the sink node can be powered by a wire connection in actual use, and the positioning technology in the water body can also achieve a high degree of accuracy, the conditions in actual use are very close to such ideal conditions.

[0038] In this embodiment, the three-dimensional static underwater acoustic sensor system based on fuzzy logic can be installed in shallow water, so the transmission loss of underwater acoustic signal can be expressed as:

[0039] TL=κ·10logr+r·10logα(f)where r represents the transmission range, the first term on the right side is the diffusion loss, and the second term is the absorption loss.

[0040] where r represents the transmission range, the value of κ is determined according to the type of wave propagation, and a(f) is the absorption coefficient. In this study, κ=1.5. In the low frequency range (100-3 kHz), the absorption coefficient can be calculated by Thorp formula as:

[0041]

[0042] ​where 10loga(f) represents the absorption coefficient in logarithmic form with units of dB / km and the frequency f is in kHz.

[0043] Sonar technology is based on the propagation characteristics of sound waves in underwater environments, and its effectiveness is closely related to the accurate measurement of sound velocity. Sound velocity is affected by environmental parameters such as seawater temperature, pressure (related to depth), and salinity, and can be represented by the following equation:

[0044] c = 1448.96 + 4.591T - 0.05304T 2 + 0.0002374T 3 + 1.340(S - 35)) + 0.0163D + 1.675 x 10 -7 D 2 - 0.01025T(S - 35) - 7.139 x 10 -1 3TD 3

[0045] c represents the sound velocity in seawater, T represents the temperature, S represents the salinity, and D represents the depth. The equation uses the range -3℃ ≤ T ≤ ℃, 33‰ ≤ S ≤ 37‰, and 0 ≤ D ≤ 8000.

[0046] When using a three-dimensional static underwater acoustic sensor system based on fuzzy logic, the network initialization phase can be performed first. In this phase, the sink node located on the sea surface initiates network initialization by broadcasting HELLO information packets to nodes within its communication range. These messages contain the sink node's identity (ID) and its unlimited remaining energy information, intended to inform surrounding sensor nodes.

[0047] Once the sensor nodes receive the HELLO information from the sink node, they will forward these messages in a broadcast manner, while adding their own node identity (ID) and current remaining energy. This process will continue until the HELLO information covers all reachable sensor nodes.

[0048] Sensor nodes that receive the HELLO information will respond by sending an acknowledgement (ACK) message. The ACK message includes the receiving node's identity (ID), its own remaining energy, and the calculated distance information between the receiving node and the sending node, which is crucial for subsequent routing and network topology construction.

[0049] During the initialization process, each node maintains a neighbor node information table, which is regularly updated to share and synchronize the latest status in the network. This mechanism not only helps establish preliminary communication relationships between nodes, but also provides adaptability for dynamic changes in the network, ensuring network stability and reliability.

[0050] After the network initialization phase is completed, the three-dimensional static underwater acoustic sensor network can start working based on fuzzy logic.

[0051] The final task of the three-dimensional static underwater acoustic sensor network working based on fuzzy logic is to send the data packet to be forwarded collected by a sensor node to the sink node directly or after being forwarded by at least one other sensor node. The sink node can directly store or process the received data packet to be forwarded or send the data packet to be forwarded to the upper computer for storage or processing.

[0052] In this embodiment, the data packet to be forwarded needs to pass through at least one sending period to reach the destination, i.e., the sink node. In each sending period, there is a sender and a receiver, wherein the sender can be one of the sensor nodes, and the receiver can be another sensor node or the sink node. For each sending period, the receiver is a specific sensor node, and a suitable network node (which can be another sensor node or the sink node) needs to be found as the next hop node.

[0053] In this embodiment, taking one of the sending periods as an example, as shown in Figure 2 , at this time, the sensor node 3 collects or receives the data packet to be sent from other sensor nodes, i.e., the sensor node 3 is the sender in this sending period, and the sensor node 3 becomes the first network node in this sending period. In addition to the sensor node 3, the network nodes including the sensor node 1, the sensor node 2, the sensor node 4, …, the sensor node n, and the sink node are the objects that the sensor node 3 needs to find as the next hop node, i.e., the sensor node 1, the sensor node 2, the sensor node 4, …, the sensor node n, and the sink node become the second network node in this sending period; or, the range of the second network node is limited, and the neighbor nodes (e.g., the network nodes that can communicate with the sensor node 3) of the sensor node 3, i.e., the first network node, are called the second network node, for example Figure 2 , the sensor node 1 and the sensor node 4 can communicate with the sensor node 3, i.e., the first network node, so the sensor node 1 and the sensor node 4 become the second network node in this sending period.

[0054] In this embodiment, referring to Figure 2 and Figure 3 , in a sending period, the first network node and the second network node perform the following steps:

[0055] S1. The first network node broadcasts a request forwarding message;

[0056] S2. The second network node receives the broadcast request forwarding message, responds to the request forwarding message, obtains its own node parameter combination, performs the first fuzzy logic processing on the node parameter combination, obtains its own first fuzzy output variable, and broadcasts the first fuzzy output variable.

[0057] S3. The first network node receives the first fuzzy output variables sent by each of the second network nodes, and determines one of the second network nodes as the next-hop node based on the first fuzzy output variables.

[0058] like Figure 2 As shown, the first network node at this time is sensor node 3, and sensor node 3 generates a request forwarding message (“State Request” packet). In this embodiment, the content of the request forwarding message (“State Request” packet) includes: the ID of the first network node, i.e., sensor node 3 (Requesting Node ID), the ID of the target node (Destination Node ID) (which can be read by sensor node 3 from its neighbor node information table), and the node location of the first network node, i.e., sensor node 3 (Requesting Node Location) (which can be represented as coordinates in the coordinate system of the water body), etc.

[0059] The first network node, namely sensor node 3, executes step S1, broadcasting the request forwarding message (“State Request” packet). For example... Figure 2 As shown, the second network node, which is the neighbor node of sensor node 3, including sensor node 1 and sensor node 4, can receive request forwarding messages (“State Request” packets).

[0060] Each second network node executes step S2. In this embodiment, taking sensor node 1 as an example, sensor node 1 receives a request forwarding message (“State Request” packet), responds to the request forwarding message (“State Request” packet), obtains its own node parameter combination, performs first fuzzy logic processing on the node parameter combination, obtains its own first fuzzy output variable 1, and broadcasts the first fuzzy output variable 1.

[0061] In this embodiment, the node parameter combination 1 that sensor node 1 needs to acquire includes sensor node 1's own REN parameter 1, DIS parameter 1, DEF parameter 1, PDR parameter 1, PDRN parameter 1, and SNHN parameter 1. The meanings of these parameters are as follows:

[0062] a. REN (residual energy) parameter: the residual energy value of the second network node; the larger (High) the REN parameter is, the better;

[0063] b. DIS (distance) parameter: the actual distance from the second network node to the first network node; the Farther the DIS parameter is, the fewer the total hop numbers of the data packet transmission are, and the more energy-saving it is;

[0064] c. DEF (deviation factor) parameter: the shortest distance from the second network node to the first network node; the Smaller the DEF is, the more the transmission path of the data packet conforms to the straight-line path of source->sink, i.e. the smaller the deviation factor is, the more energy-saving it is;

[0065] d. PDR (packet delivery ratio) parameter: the packet delivery ratio between the second network node and the primary candidate neighbor node of the second network node; the larger the PDR is, the better;

[0066] e. PDRN (package delivery rate of the next hop node) parameter: the packet delivery ratio between the primary candidate neighbor node and the secondary candidate neighbor node of the second network node; the larger the PDRN is, the better;

[0067] f. SNHN (suitability of the next hop node) parameter: the maximum value of the fuzzy output variable of the primary candidate neighbor node of the second network node; the larger the SNHN is, the better.

[0068] Therefore, the node parameter combination of the sensor node 1 to be obtained by the sensor node 1 itself includes the REN parameter 1 (the residual energy value of the sensor node 1), the DIS parameter 1 (the actual distance from the sensor node 1 to the first network node, i.e. the sensor node 3), the DEF parameter 1 (the shortest distance from the sensor node 1 to the first network node, i.e. the sensor node 3), the PDR parameter 1 [the packet delivery ratio between the sensor node 3 and the primary candidate neighbor node of the sensor node 1, i.e. the candidate neighbor node of the sensor node 1 itself], the PDRN parameter 1 [the packet delivery ratio between the primary candidate neighbor node of the sensor node 1, i.e. the candidate neighbor node of the sensor node 1 itself, and the secondary candidate neighbor node, i.e. the candidate neighbor node of the candidate neighbor node of the sensor node 1 itself], and the SNHN parameter 1 [the maximum value of the fuzzy output variable of the primary candidate neighbor node of the sensor node 1, i.e. the candidate neighbor node of the sensor node 1 itself].

[0069] PDRN parameter 1 not only considers the packet loss rate of the possible next hop node (i.e. sensor node 1) to be investigated by sensor node 3, but also deeply analyzes the packet loss of the possible next next hop node (i.e. the neighbor node of sensor node 1). Through this double consideration, the risk of the link encountering a signal blank area is effectively reduced, thereby significantly reducing the overall packet loss rate; SNHN parameter 1 considers the link quality of the next next hop node (i.e. the neighbor node of sensor node 1), which is beneficial to ensure the improvement of the overall link quality.

[0070] After sensor node 1 performs step S2 to obtain node parameter combination 1, it performs first fuzzy logic processing on node parameter combination 1 to obtain a corresponding processing result, i.e. first fuzzy output variable 1 (denoted as Suitability1), and then generates a data packet State Response1, fills first fuzzy output variable 1 (Suitability1) into the value field of data packet State Response1, and broadcasts data packet State Response1. Since sensor node 1 is a neighbor node of sensor node 3, sensor node 3 can receive data packet State Response1 and obtain first fuzzy output variable 1 (Suitability1) from data packet State Response1.

[0071] In this embodiment, the first fuzzy logic processing performed by the second network node when performing step S2 is a mechanism based on fuzzy logic that can coordinate and analyze contradictory parameters to intelligently combine approximate, imprecise and uncertain information. The main steps to realize its mechanism are to first fuzz the precise input values of the network system, then obtain fuzzy conclusions according to the pre-set fuzzy reasoning rule base by taking the obtained fuzzy input values as conditions, and finally obtain precise outputs through defuzzification. Specifically, the fuzzy logic used by the first fuzzy logic processing is as follows: there are 6 fuzzy input variables, which are REN (High, Medium, Low), DIS (Far, Moderate, Close), DEF (Small, Average, Far), PDR (Good, Bad), PDRN (Well, Poor), and SNHN (Great, Acceptable, Inferior); the fuzzy output variable is Suitability (PS, HS, S, MS, N, MU, U, HU, CU); the content in the parentheses is the language variable of the variable, and its specific meaning is as follows:

[0072] CU: Completely Unsuitable - completely unsuitable;

[0073] HU: Highly Unsuitable

[0074] U: Unsuitable - not suitable;

[0075] MU: Marginally Unsuitable - Barely suitable;

[0076] N: Neutral - barely adequate / sufficient;

[0077] MS: Marginally Suitable - Barely suitable;

[0078] S: Suitable;

[0079] HS: Highly Suitable - Very suitable;

[0080] PS: Perfectly Suitable.

[0081] In this embodiment, the principle of performing first fuzzy logic processing on fuzzy input variables such as REN, DIS, DEF, PDR, PDRN, and SNHN to obtain the fuzzy output variable Suitability is as follows: Figure 4 As shown in Table 1. Among them, Figure 4 Part (a) is the membership function corresponding to the REN parameter. Figure 4 Part (b) is the membership function corresponding to the DIS parameter. Figure 4 Part (c) is the membership function corresponding to the DEF parameter. Figure 4 The (d) part is the membership function corresponding to the PDR parameter. Figure 4 The (e) part is the membership function corresponding to the PDRN parameters. Figure 4 The (f) part is the membership function corresponding to the SNHN parameters. Figure 4 The (g) part is the membership function corresponding to the fuzzy output variable Suitability.

[0082] Table 1

[0083]

[0084] Taking the first fuzzy logic processing of node parameter combination 1 by one of the second network nodes, namely sensor node 1, as an example, if the values ​​of REN parameter 1, DIS parameter 1, DEF parameter 1, PDR parameter 1, PDRN parameter 1, and SNHN parameter 1 in node parameter combination 1 are "High", "Far", "Small", "Good", "Well", and "Acceptable" respectively, then according to Figure 4The membership function shown, which satisfies rule 2 in Table 1, can output the value of the first fuzzy output variable 1 (Suitability 1) as "HS".

[0085] Another second network node, i.e. sensor node 4, also performs step S2, thereby obtaining a node parameter combination 4 (including REN parameter 4, DIS parameter 4, DEF parameter 4, PDR parameter 4, PDRN parameter 4, and SNHN parameter 4, etc.), and performing first fuzzy logic processing on the node parameter combination 4 to obtain a corresponding processing result, i.e. first fuzzy output variable 4 (denoted as Suitability 4), and then generating a data packet State Response 4, filling the first fuzzy output variable 4 (Suitability 4) into the value field of the data packet State Response 4, and broadcasting the data packet State Response 4. Since sensor node 4 is a neighbor node of sensor node 3, sensor node 3 can receive the data packet State Response 4 and obtain the first fuzzy output variable 4 (Suitability 4) from the data packet State Response 4.

[0086] In step S3, after receiving the first fuzzy output variable 1 (Suitability 1) sent by sensor node 1 and the first fuzzy output variable 4 (Suitability 4) sent by sensor node 4, etc., the first network node, i.e. sensor node 3, selects one second network node as a next hop node from sensor node 1, sensor node 4, etc. according to the respective first fuzzy output variables Suitability 1, Suitability 4, etc.

[0087] In this embodiment, when the first network node performs step S3, i.e. determines one of the second network nodes as the next hop node according to the respective first fuzzy output variables, the following steps can be performed:

[0088] S301A. Determine the maximum value in the respective first fuzzy output variables;

[0089] S302A. Determine the second network node sending the maximum value as the next hop node.

[0090] Steps S301A-S302A are a first execution manner of step S3.

[0091] In step S301A, the first network node, i.e., the sensor node 3, finds the maximum value among the received first fuzzy output variables 1 (Suitability1), 4 (Suitability4), and so on. The value of the first fuzzy output variable increases in the order of CU, HU, U, MU, N, MS, S, HS, and PS. For example, if the value of one first fuzzy output variable is HS and the value of another first fuzzy output variable is MS, the first fuzzy output variable with the value of HS is greater than the first fuzzy output variable with the value of MS. Assume that the maximum value is the first fuzzy output variable 1 (Suitability1).

[0092] In step S302A, the first network node, i.e., the sensor node 3, determines the second network node, i.e., the sensor node 1, that sends the maximum value, i.e., the first fuzzy output variable 1 (Suitability1), and thus determines the sensor node 1 as the next hop node.

[0093] In this embodiment, by performing steps S1-S3, the first network node in each sending period can calculate the suitability value by using the fuzzy weight function to find the next hop node that is most suitable for forwarding the data packet. In the case of high node density, the energy consumption and data processing time can be effectively saved, and the overall link quality can be improved, thus realizing the energy-saving-oriented routing protocol. Moreover, by introducing the PDR parameter, the PDRN parameter, and the SNHN parameter in the node parameter combination to be processed when performing the first fuzzy logic processing, it is possible to avoid the communication from entering the blank area and improve the packet delivery rate.

[0094] In this embodiment, when the first network node performs step S3, i.e., determines one of the second network nodes as the next hop node according to the first fuzzy output variables, the following steps can be performed:

[0095] S301B. Perform second fuzzy logic processing on the first fuzzy output variables to obtain second fuzzy output variables;

[0096] S302B. Determine one of the second network nodes as the next hop node according to the second fuzzy output variables.

[0097] Steps S301B-S302B are a second execution mode of step S3.

[0098] In step S301B, the first network node, i.e., the sensor node 3, performs second fuzzy logic processing on each first fuzzy output variable, Suitability1, Suitability4, etc., to obtain a second fuzzy output variable. In this embodiment, when performing the second fuzzy logic processing, the membership function of each first fuzzy output variable is as shown in (g) of FIG. 6, and the membership function of the second fuzzy output variable is as shown in (h) of FIG. 6, so that the second fuzzy output variable, which can take a first value (YES) or a second value (NO), is obtained. Figure 5 Figure 6

[0099] In this embodiment, if the second fuzzy output variable obtained in step S301B is the first value (YES), then in step S302B, the second network node that sends the maximum value of each first fuzzy output variable is determined as the next hop node, as in steps S301A-S302A. If the second fuzzy output variable obtained in step S301B is the second value (NO), then steps S301A-S302A are not performed, and the next hop node is not directly determined by other means, but the first network node waits for a first time duration (the first time duration can be the length of one or more sending periods) before performing steps S1-S2 again to obtain new first fuzzy output variables, and then performing step S301B to obtain a new second fuzzy output variable based on the new first fuzzy output variables. If the new second fuzzy output variable is the first value (YES), then the second network node that sends the maximum value of the new first fuzzy output variables is determined as the next hop node, as in steps S301A-S302A. Otherwise, the first network node waits for the first time duration again and performs steps S1-S2 again, and so on, until the next hop node is determined, or the total number of times of waiting for the first time duration reaches a threshold number of times.

[0100] In this embodiment, the principle of performing steps S301B-S302B is that, by performing second fuzzy logic processing on each first fuzzy output variable, the second fuzzy output variable obtained represents the availability of each first fuzzy output variable, where the second fuzzy output variable is the first value (YES) when each first fuzzy output variable is available, so that the next hop node can be determined based on each first fuzzy output variable according to steps S301A-S302A. Conversely, when the second fuzzy output variable is the second value (NO), each first fuzzy output variable is unavailable, and the first network node waits for the first time duration, so that the parameters of the first network node and each second network node change, and then the updated first fuzzy output variables are obtained for judgment. Therefore, by performing steps S301B-S302B, the available next hop node can be selected, and the node packet delivery rate is guaranteed.

[0101] In this embodiment, as shown in​​​ As shown, assuming that the first network node, i.e. the sensor node 3, determines the sensor node 1 as the next hop node, the sensor node 3 sends the data packet to be forwarded to the next hop node, i.e. the sensor node 1. When the next sending cycle is performed, the sensor node 1 becomes the new first network node, and the neighbor nodes of the sensor node 1 become the new second network nodes. The steps S1-S3 are performed by the new first network node and the new second network nodes, and finally the data packet to be forwarded is sent to the sink node.

[0102] In this embodiment, a retransmission mechanism can be provided. Specifically, if the first network node does not receive the acknowledgement from the next hop node within a predetermined time, the first network node can retransmit the data packet with the RC field value +1. When the RC recorded by the first network node reaches a preset maximum retransmission number (e.g. 3), the data packet will be discarded, the first network node finds the second highest value in each first fuzzy output variable, and determines the second network node sending the second highest value as the reselected next hop node, and sends the data packet to be forwarded to the reselected next hop node.

[0103] When the data packet to be forwarded reaches the sink node, or reaches the maximum retransmission number, the data transmission process is ended.

[0104] It should be noted that, unless otherwise specified, when a certain feature is referred to as being "fixed" or "connected" to another feature, it can be directly fixed or connected to the other feature, or indirectly fixed or connected to the other feature. In addition, the up, down, left, right and other descriptions used in the present disclosure are only relative to the relative positional relationship of the components of the present disclosure in the drawings. The singular forms "a", "an" and "the" used in the present disclosure are also intended to include the plural forms, unless the context clearly indicates otherwise. In addition, unless otherwise defined, all technical and scientific terms used in the present embodiment are the same as those commonly understood by those skilled in the art. The terms used in the present embodiment are only used to describe the specific embodiments, and are not intended to limit the present application. The term "and / or" used in the present embodiment includes any combination of one or more related listed items.

[0105] It should be understood that although the terms first, second, third, etc. can be used in the present disclosure to describe various elements, these elements should not be limited to these terms. These terms are only used to distinguish one type of element from another. For example, without departing from the scope of the present disclosure, a first element can also be referred to as a second element, and similarly, a second element can also be referred to as a first element. The use of any and all examples or exemplary language (e.g., "for example", "as such", etc.) provided in the present embodiment is only intended to better illustrate the embodiments of the present application, and unless otherwise required, does not impose any limitation on the scope of the present application.

[0106] It should be appreciated that embodiments of the present application can be realized by computer hardware, a combination of hardware and software, or by computer instructions stored on a non-transitory computer-readable storage medium. The methods can be implemented in a computer program product by use of standard programming techniques in the computer programming arts - including non-transitory computer-readable storage media configured with computer programs to cause a computer to operate in a specific and predefined manner according to the methods described in the detailed embodiments and the accompanying drawings. Each program can be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the programs can be implemented in assembly or machine language, if desired. In any case, the language can be a compiled or interpreted language. Also, the programs can be able to run on a specially programmed integrated circuit.

[0107] Further, the operations of the processes described in the embodiments can be performed in any suitable order, unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described in the embodiments (or variations and / or combinations thereof) can be implemented under the control of one or more computer systems configured with executable instructions (e.g., computer programs, one or more computer programs, or one or more applications), and can be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) executing collectively on one or more processors, by hardware, or combinations thereof. Computer programs include machine instructions that can be executed by one or more processors.

[0108] Further, the methods can be implemented in any suitable type of computing platform operably connected to, including but not limited to, a personal computer, mini-computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platforms, or in communication with charged particle tools or other imaging devices, and the like. Aspects of the present application can be implemented in machine readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optical read and / or write storage media, RAM, ROM, and the like, such that it can be read by a programmable computer and used to configure and operate the computer to perform the processes described herein when the storage medium or device is read by the computer. Further, the machine readable code, or portions thereof, can be transmitted over wired or wireless networks. The present application includes these and other different types of non-transitory computer readable storage media when the instructions or programs implementing the above steps are included in conjunction with microprocessors or other data processors. The present application also includes the computer itself when programmed according to the methods and techniques of the present application.

[0109] A computer program can be applied to input data to perform the functions of the present embodiments to transform the input data to generate output data that is stored to non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the present application, the transformed data represents a physical and tangible object, including a particular visual depiction of a physical and tangible object produced on a display.

[0110] The above merely preferred embodiments of the present application and are not intended to limit the present application. The present application can be variously modified and changed without departing from the spirit and scope of the present application. Therefore, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. A fuzzy logic based three-dimensional static hydroacoustic sensor system, characterized by, The fuzzy logic-based three-dimensional static underwater acoustic sensor network comprises a plurality of network nodes, including a first network node and a plurality of second network nodes; The first network node and the second network nodes are configured to perform a plurality of processing procedures until a next hop node is determined, or a total number of waiting for a first time length reaches a number threshold; In any processing procedure: The first network node broadcasts a request forwarding message; The second network nodes receive the broadcast request forwarding message, obtain a node parameter combination of each second network node in response to the request forwarding message, perform first fuzzy logic processing on the node parameter combination to obtain a first fuzzy output variable of each second network node, and broadcast the first fuzzy output variable; The first network node receives the first fuzzy output variable sent by each second network node, performs second fuzzy logic processing on each first fuzzy output variable to obtain a second fuzzy output variable, determines a maximum value in the first fuzzy output variable when the second fuzzy output variable is a first value, and determines the second network node sending the maximum value as the next hop node; when the second fuzzy output variable is a second value, the first network node waits for a first time length and then performs a next processing procedure.

2. The fuzzy logic based three-dimensional static hydrophone sensor system of claim 1, wherein, The obtaining of the node parameter combination of each second network node comprises: Obtaining a REN parameter, a DIS parameter, a DEF parameter, a PDR parameter, a PDRN parameter and a SNHN parameter of each second network node; The REN parameter represents a residual energy value of the second network node, the DIS parameter represents an actual distance between the second network node and the first network node, the DEF parameter represents a shortest distance from the second network node to the first network node, the PDR parameter represents a packet delivery rate between the second network node and a primary candidate neighbor node of the second network node, the PDRN parameter represents a packet delivery rate between the primary candidate neighbor node and a secondary candidate neighbor node of the second network node, and the SNHN parameter represents a maximum value of a fuzzy output variable of the primary candidate neighbor node of the second network node. The first network node is configured to obtain a data packet to be forwarded and send the data packet to the next hop node.

3. The fuzzy logic based three-dimensional static hydrophone sensor system of claim 1, wherein, The plurality of network nodes comprises a sink node and a plurality of sensor nodes.

4. The fuzzy logic based three-dimensional static hydrophone sensor system according to any of claims 1-3, characterized in that, Any sensor node is configured to serve as the first network node, and the sink node or any sensor node is configured to serve as the second network node.

5. The fuzzy logic based three-dimensional static hydrophone sensor system of claim 4, wherein, The sink node is configured to be arranged on a water surface of a water body, each sensor node is configured to be arranged in an underwater three-dimensional space of the water body, and each sensor node is randomly distributed in the underwater three-dimensional space according to a Poisson distribution.

6. The fuzzy logic based three-dimensional static hydrophone sensor system of claim 5, wherein, ​

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